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Rotation-invariance is essential for accurate detection of spatially variable genes in spatial transcriptomics

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  • Haohao Su

    (Michigan State University)

  • Yuehua Cui

    (Michigan State University)

Abstract

In spatial transcriptomics, tissue samples are randomly positioned. Rotation-sensitive methods can lead to unreliable spatially variable gene (SVG) detection. We highlight their inherent technical pitfalls and discuss strategies for rotation-invariant methods, enhancing the robustness of SVG detection.

Suggested Citation

  • Haohao Su & Yuehua Cui, 2025. "Rotation-invariance is essential for accurate detection of spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-5, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62574-4
    DOI: 10.1038/s41467-025-62574-4
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    References listed on IDEAS

    as
    1. Alexis Vandenbon & Diego Diez, 2020. "A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Souvik Seal & Benjamin G Bitler & Debashis Ghosh, 2023. "SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data," PLOS Genetics, Public Library of Science, vol. 19(10), pages 1-25, October.
    3. Guanao Yan & Shuo Harper Hua & Jingyi Jessica Li, 2025. "Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
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